Since OU quantifies the deviation of average velocity of each tray from the designed velocity, a higher OU value indicates that the crops will have better and more uniform photosynthesis. It can be observed from Fig. 5 that the maximum OU obtained for all conditions is case BC at a flow rate of 0.3 kg s−1. To develop a better understanding, the two-dimensional velocity and vorticity distributions in the x-y plane along the middle of the z-direction for all eight cases at a mass flow rate of 0.3 kg s−1 are plotted in Figs. 6 and 7. As can be observed from Figs. 6 to 7, the OU is highest for case BC due to its uniform velocity and vorticity distributions between trays.For case BC, the inlet flow is parallel to the longitudinal direction of the tray and the exit is along the transverse direction . This design allows the flow to travel through the long side of the tray uninterrupted and then form a helical flow orientation near the end of the tray. This spiral formation of flow induces a more uniform and regular flow in the room. This also explains why case AD has very high OU. Similar spiral formation can also be observed when the inlet flow is parallel to the transverse direction of the tray and the exit is along the longitudinal direction , like case DA. However, since the inlet flow is along the short side of the tray, the benefit is not as great and requires much higher inlet mass flow rate. On the other hand, for cases where the inlet and exit are located on the same wall, such as AB or CD, the air flow only has strong mixing effect along the inlet/exit direction which, in turn,cannabis grow supply store reduces the overall flow uniformity. Besides the velocity distribution, the effect of temperature is also a critical parameter for determining convective flow. Fig. 8 shows the two-dimensional temperature distributions in the x-y plane along the middle of the z-direction for all eight cases at a mass flow rate of 0.3 kg s−1.
In our analysis, the temperature of the inlet flow is lower than that of the exit flow due to the heat generated from the LED light. For case BC, the inlet is located near the bottom and the exit is near the top. Due to the density difference, the exit warm stream tends to flow up. This allows the flow to reach the topmost tray more easily and, therefore, achieves more uniform temperature distribution among all trays. Combining the inlet flow along the long side of the tray, the helical flow effect, and the buoyancy, case BC is able to reach the maximum OU of 91.7%. Fig. 9 summarized the velocity and temperature contours for case BC at an inlet mass flow rate of 0.3 kg s−1. The velocity pro- files in Fig. 9a clearly show the spiral effect above each cultivation tray and the local velocity is close to the optimal speed of 0.4 m s−1. In addition, the temperature shows an increasing trend from bottom to top as the flow helically passing through the crops and moving towards the outlet.The distributions of temperature and gas species, such as water vapor and CO2, play an integral role in photosynthesis which, in turn, influences the quality of plant and its growth. Therefore, maintaining these critical parameters in a reasonable range to ensure reliable and efficient production is essential to environmental control of an IVFS. Evaluating the distribution of these parameters can also provide the effectiveness of inlet/exit location. It should be noted that the parameter OU provides an overall assessment of the air flow velocity over planting trays. An optimal design is to achieve desired local temperature and species distribution while maintaining high OU values in an IVFS. In the following discussion, the four cases with highest values of OU at their corresponding mass flow rates are studied and compared to the baseline case AB.Since CO2 is a reactant of photosynthesis, increasing CO2 concentration usually leads to enhancement of crop production. Reports show that increasing the CO2 concentration from the atmospheric average of 400 ppm to 1500 ppm can increase the yield by as much as 30%. In this IVFS analysis, the CO2 level of the inlet mass flow rate is increased by a CO2 generator to be 1000 ppm . Since the consumption rate of CO2 through the exchange zones is fixed, higher overall average CO2 concentration through the system is desirable. Fig. 10 shows the comparison of the average CO2 concentration between the highest OU cases and the baseline case AB at different inlet mass flow rate.
A few general trends of CO2 concentration can be observed from Fig. 10. First, the CO2 concentration increases with inlet flow rate due to increasing supply of CO2 molecules. In addition, tray 1 has the highest CO2 concentration because most of the cold fresh inlet air dwells near the bottom of the IVFS due to the buoyancy effect. In contrast, tray 3 has the lowest CO2 concentration because the fresh inlet air has the highest flow resistance to reach tray 3due to the combination of sharp turns and buoyancy effect. This is particularly true at low inlet flow rates and when the inlet is located on the top, which lead to low flow circulation as cold inlet air flows downward directly. As a result, BC, BA, and DA at 0.3, 0.4, and 0.5 kg s−1, respectively, have relatively high CO2 concentrations. Even though the baseline case AB at 0.5 kg s−1 has the highest CO2 concentration, its OU is too low to be considered a good design.According to Eq. , the power required can be calculated as the product of volume flow rate and pressure drop between the inlet and exit. Even though the inlet/exit locations can change the overall system pressure drop slightly, mass flow rate has a dominating effect on the required power, as shown in Fig. 13. It can be observed that cases AB and BA incur the most pressure drop, which is more obvious at high flow rates. As discussed earlier, placing the inlet and exit on the same wall located at the short side disrupts the helical flow formation, which is known to benefit flow circulation. Under this condition , additional vertices, which is the main source of the increased pressure drop, are observed near the exit region from the flow streamlines. To minimize energy consumption, the inlet/exit location should be placed on opposite walls and the IVFS system should operate at the minimum flow rate that meets other requirements, such as temperature, RH, CO2 concentration, and flow velocity above the exchange region. Since there are multiple variables that can affect the overall design of the inlet/exit location and mass flow rate, an overall efficiency factor is introduced in Eq. to holistically assess the uniformity of all monitored parameters. Fig. 14 shows the final comparison of the overall design efficiency between the four best OU cases and the baseline case. It can be observed that OU has a dominating effect on the overall efficiency since the trends show some resemblance to the overall efficiency.
In terms of overall efficiency and power consumption, case BC operating at 0.3 kg s−1 is the optimal design for this IVFS.A comprehensive understanding of the biological networks at multiple levels is crucial to harness the full potential of ENMs for sustainable food production . In recent years, probing of ENM-plant interaction have evolved from traditional, single endpoint assays to discovery oriented, high-throughput system biology approaches, referred as “omics”. This is supported by advancements in the sensitivity and accuracy of analytical techniques and bio-informatic tools . The suffix “omics” refers to unbiased screening of bio-molecules in an organism, specifically genes , mRNA , proteins, or metabolites . Systems biology approach has been implemented to decode the molecular mechanisms in plants and elucidate the behavior of genes, proteins and metabolites in response to biotic or abiotic stressors . With the emerging need for mechanistic understanding of complex agronomic traits and crops’ response to ENM exposure, omic technologies have gained momentum in precision agriculture and nanotoxicity studies. This paradigm helps in generating hypotheses by monitoring response of biomolecules upon systematically perturbing biological processes with ENMs, followed by integration of global datasets onto pathways using advanced bioinformatics algorithms . Realization of the underlying molecular mechanisms in plants will provide cues for designing ENMs for specific applications like increasing resilience to pests or environmental conditions,cannabis drainage system targeted delivery of nutrients or pesticides, stimuli-responsive agrochemical release, or ENM-enabled biosensing. The sensitivity of omic techniques allows to capture, quantitate and distinguish the cellular and molecular level changes in an organism when exposed to ionic, nano- or bulk- form of any particle of interest at considerably lower and environmentally realistic doses; these deductions are not obvious from phenotypic responses or less sensitive biochemical assays . These techniques also allow to compare responses at multiple hierarchical levels across different plant species, age, growth/environmental conditions, and ENM exposures. In plants, transcriptomics has been the most applied omic technique, used to identify the transcription factors as predictive biomarkers of ENM toxicity , which are correlated to phenotypic responses. However, this bottom-up approach based on upward chain of causality has several constraints that result in inconclusive nature of such approach . These constraints emerge from the uncertainty resulting from post transcriptional processes, post translational protein modifications, and stimulus-induced metabolite level changes. The higher level of organization representing the metabolome or proteome in an organism are not fully determined by the properties of the lower levels ; instead, they regulate the functionality of lower levels in a downward causation chain in response to stimuli . Metabolomic analyses allow functional annotation of uncharacterized genes or proteins, thereby filling knowledge gaps in plant metabolic machinery.
In addition, metabolomics does not depend on the data generated from model plant species, hence could be easily applied to model as well as non-model species . Thus, due to the exploratory nature of ENM-plant interaction studies, it is recommended to follow the downward causation approach that correlates phenotypic expression with the metabolome of plants, which can complement proteomic and transcriptomic profiles in a pathway analysis network. This review consolidates the pioneering studies in plant metabolomics and proteomics, intended to gain insights into ENM-plant interactions. Studies employing other omic tools have been discussed briefly. We discuss novel analytical platforms employed in metabolomics and proteomics in plants in response to ENMs and address the important factors in such analyses. Finally, we postulate the vulnerable biological pathways in plants in response to ENMs and how integration of multiomic datasets can be exploited to address major mechanistic concerns and enable realization of wider application of nanotechnology in agriculture.Metabolites are the end-product of cellular regulatory processes that reflect the ultimate response of an organism to any external stimulus . The plant metabolic pathway databases, curated from experimental literature by the Plant Metabolic Network , report 4,544 compounds involved in 1,123 pathways across 350 plant species . Plants collectively produce a diverse array of 200,000 metabolites, which are broadly divided into two major categories, primary and secondary metabolites . Primary metabolites, which include carbohydrates, amino acids, vitamins, organic acids, and fatty acids, are required for plant growth and development . Secondary metabolites are synthesized from the primary metabolites for adaptation and defense response in plants . The major classes of secondary metabolites are polyketides, terpenoids, steroids, phenylpropanoids, alkaloids, and glucosinolates, which have their own biogenetic pathways and thousands of products and pathway intermediates . Primary metabolites are universal and conserved in their structures throughout the plant kingdom, whereas the secondary metabolites are species-specific and differ in chemical complexity. Any alteration in plant physiology in response to a xenobiotic, such as ENMs, is regulated by molecular events and is reflected at the level of metabolites that participate in interconnected biological pathways such as glycolysis, citric acid cycle, gluconeogenesis, biosynthesis of amino acids, biosynthesis of secondary metabolites, nitrogen metabolism, and fatty acid metabolism. Plant roots also exude metabolites as signaling molecules to defend or adapt to stressors as well as modulate soil chemistry and/or biochemical pathways to influence nutrient bio-availability .